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# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
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* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
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## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at chris@fregly.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
[homepage]: http://contributor-covenant.org
[version]: http://contributor-covenant.org/version/1/4/
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Want to become a Committer?
Click [HERE](http://pipeline.io/contribute) for details on contributing to PipelineAI.
201 LICENSE
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105 README.md
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![PipelineAI Logo](https://pipeline.ai/assets/img/pipelineai.png)
# PipelineAI Quick Start (CPU + GPU)
Train and Deploy your ML and AI Models in the Following Environments:
* [Hosted Community Edition](https://quickstart.pipeline.ai/community)
* [Docker](https://quickstart.pipeline.ai/docker)
* [Kubernetes](https://quickstart.pipeline.ai/kubernetes)
* [AWS SageMaker](https://quickstart.pipeline.ai/sagemaker)
# Having Issues? Contact Us Anytime... We're Always Awake.
* Slack: https://joinslack.pipeline.ai
* Email: [help@pipeline.ai](mailto:help@pipeline.ai)
* Web: https://support.pipeline.ai
* YouTube: https://youtube.pipeline.ai
* Slideshare: https://slideshare.pipeline.ai
* Workshop: https://workshop.pipeline.ai
* [Troubleshooting Guide](/docs/troubleshooting)
# PipelineAI Community Events
* [PipelineAI Monthly Webinar (TensorFlow + Spark + GPUs + TPUs)](https://webinar.pipeline.ai)
* [Advanced Spark and TensorFlow Meetup (Global)](https://meetup.pipeline.ai)
# PipelineAI Products
* [Features](https://pipeline.ai/features)
* [Products](https://pipeline.ai/products)
## Consistent, Immutable, Reproducible Model Runtimes
![Consistent Model Environments](https://pipeline.ai/assets/img/docker-gobbles-ml.png)
Each model is built into a separate Docker image with the appropriate Python, C++, and Java/Scala Runtime Libraries for training or prediction.
Use the same Docker Image from Local Laptop to Production to avoid dependency surprises.
## Sample Machine Learning and AI Models
Click [**HERE**](https://github.com/PipelineAI/models/tree/master) to view model samples for the following:
* Scikit-Learn
* TensorFlow
* Keras
* Spark ML (formerly called Spark MLlib)
* XGBoost
* PyTorch
* Caffe/2
* Theano
* MXNet
* PMML/PFA
* Custom Java/Python/C++ Ensembles
![Nvidia GPU](https://pipeline.ai/assets/img/nvidia-cuda-338x181.png) ![TensorFlow](https://pipeline.ai/assets/img/tensorflow-logo-202x168.png)
![Spark ML](https://pipeline.ai/assets/img/spark-logo-254x163.png) ![Scikit-Learn](https://pipeline.ai/assets/img/scikit-logo-277x150.png)
![R](https://pipeline.ai/assets/img/r-logo-280x212.png) ![PMML](https://pipeline.ai/img/pmml-logo-210x96.png)
![Xgboost](https://pipeline.ai/assets/img/xgboost-logo-280x120.png) ![Model Ensembles](https://pipeline.ai/assets/img/ensemble-logo-285x125.png)
## Supported Model Runtimes (CPU and GPU)
* Python (Scikit, TensorFlow, etc)
* Java
* Scala
* Spark ML
* C++
* Caffe2
* Theano
* TensorFlow Serving
* Nvidia TensorRT (TensorFlow, Caffe2)
* MXNet
* CNTK
* ONNX
## Supported Streaming Engines
* Kafka
* Kinesis
* Flink
* Spark Streaming
* Heron
* Storm
## Drag N' Drop Model Deploy
![PipelineAI Drag n' Drop Model Deploy UI](https://pipeline.ai/assets/img/drag-n-drop-tri-color.png)
## Generate Optimize Model Versions Upon Upload
![Automatic Model Optimization and Native Code Generation](https://pipeline.ai/assets/img/automatic-model-optimization-native-code-generation.png)
## Distributed Model Training and Hyper-Parameter Tuning
![PipelineAI Advanced Model Training UI](https://pipeline.ai/assets/img/pipelineai-train-compare-ui.png)
![PipelineAI Advanced Model Training UI 2](https://pipeline.ai/assets/img/pipelineai-train-compare-ui-2.png)
## Continuously Deploy Models to Clusters of PipelineAI Servers
![PipelineAI Weavescope Kubernetes Cluster](https://pipeline.ai/assets/img/weavescope-with-header.png)
## View Real-Time Prediction Stream
![Live Stream Predictions](https://pipeline.ai/assets/img/live-stream-predictions.png)
## Compare Both Offline (Batch) and Real-Time Model Performance
![PipelineAI Model Comparison](https://pipeline.ai/assets/img/dashboard-batch-and-realtime.png)
## Compare Response Time, Throughput, and Cost-Per-Prediction
![PipelineAI Compare Performance and Cost Per Prediction](https://pipeline.ai/assets/img/compare-cost-per-prediction.png)
## Shift Live Traffic to Maximize Revenue and Minimize Cost
![PipelineAI Traffic Shift Multi-armed Bandit Maximize Revenue Minimize Cost](https://pipeline.ai/assets/img/maximize-revenue-minimize-costs.png)
## Continuously Fix Borderline Predictions through Crowd Sourcing
![Borderline Prediction Fixing and Crowd Sourcing](https://pipeline.ai/assets/img/fix-slack.png)
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